A Framework for Online Investment Algorithms
This work addresses portfolio management inefficiencies for investors by integrating financial theory, data analysis, and process-level learning, though it appears incremental as an enhancement to existing algorithmic approaches.
The authors tackled the problem of siloed offline investment processes by developing an online algorithmic framework for portfolio management that makes sequential updates as data arrives, which outperformed naive market capitalization benchmarks using resampling methods while highlighting back-test over-fitting risks.
The artificial segmentation of an investment management process into a workflow with silos of offline human operators can restrict silos from collectively and adaptively pursuing a unified optimal investment goal. To meet the investor's objectives, an online algorithm can provide an explicit incremental approach that makes sequential updates as data arrives at the process level. This is in stark contrast to offline (or batch) processes that are focused on making component level decisions prior to process level integration. Here we present and report results for an integrated, and online framework for algorithmic portfolio management. This article provides a workflow that can in-turn be embedded into a process level learning framework. The workflow can be enhanced to refine signal generation and asset-class evolution and definitions. Our results confirm that we can use our framework in conjunction with resampling methods to outperform naive market capitalisation benchmarks while making clear the extent of back-test over-fitting. We consider such an online update framework to be a crucial step towards developing intelligent portfolio selection algorithms that integrate financial theory, investor views, and data analysis with process-level learning.